Python in 3 Hours! [+ Machine Learning & Deep Learning]

🧠 Switch to Python, Learn Machine Learning Fundamentals, and Develop Deep Learning Models Using TensorFlow All in 3 Hours!

1.1. Course Instructor [Learn More]

My name is Mohammad H. Rafiei, Ph.D., and I am a researcher and instructor at prestigious institutions such as Johns Hopkins University, College of Engineering, and Georgia State University, Department of Computer Science. As the founder of MHR Group LLC, I lead a team tackling complex challenges in computer science, engineering, and medicine using advanced machine learning and optimization techniques. I am thrilled to share my expertise with you on Udemy as your guide through Python and its powerful applications in machine learning and deep learning.

1.2. Does this course suit you? [Sign Up]

If you're looking to master Python for data science, you've come to the right place! This comprehensive course is designed for individuals who:

• Are new to Python and want to kickstart their programming journey.
• Have prior experience with a computational language like MATLAB, R, or C++ and want to transition to Python.
• Seek to understand and apply machine learning techniques without installing complex software.
• Want to leverage the power of Google Colab for real-time data analysis and model training on the cloud.
• Are eager to explore neural networks and deep learning using TensorFlow Keras without the hassle of setting up environments.

1.4. Course Overview 📚

This course is a concise, yet comprehensive, journey through Python programming with a focus on data science applications. Spanning just 180 minutes across 12 engaging lectures, here's what you can expect:

• Lecture 01: An Introduction to the Course (18 minutes)
• Lecture 02: Getting Started with Gmail, Chrome, and Google Colab (11 minutes)
• Lecture 03: Mastering Operations, Built-in Functions, and Data Types (20 minutes)
• Lecture 04: Loops, Conditional Scripts, and Functions (16 minutes)
• Lecture 05: Data Processing with Numpy and Pandas (28 minutes)
• Lecture 06: Data Visualizations with Matplotlib and Seaborn (10 minutes)
• Lecture 07: Working with Data Repositories and Data Split in Machine Learning (15 minutes)
• Lecture 08: Data Processing and Calibrations for Machine Learning Models (13 minutes)
• Lecture 09: A Brief Introduction to Neural Networks (11 minutes)
• Lecture 10: Building Regression Neural Networks with TensorFlow Keras (16 minutes)
• Lecture 11: Crafting Classification Neural Networks with TensorFlow Keras (13 minutes)
• Lecture 12: Hit the Road on Your Own! (9 minutes)

1.5. Your Contribution 🤝

Your feedback is crucial for improvement! After taking this course, I invite you to leave a review to help us make it even better. If you find it valuable, please share it with your peers and colleagues. Together, we can empower more learners to harness the power of Python in data science and machine learning.

1.6. Acknowledgment 🙏

I extend my deepest gratitude to my wife, Fatemeh, for her unwavering support throughout the creation of this course. Special thanks to my friend and brother, Ahmad Mohammadshirazi, for his invaluable assistance in video editing. Your expertise and dedication have significantly contributed to the quality of this learning experience.

Overall Course Review

The course in question has garnered an impressive global rating of 4.41, with all recent reviews being consistently positive. It is clear from the feedback that this course is highly effective for beginners looking to delve into machine learning (ML) using Python, as well as for those with prior programming experience who wish to expand their skillset to include Python for data analysis, visualization, and ML.

Pros:

• Comprehensive for Beginners: The course is praised for its careful preparation, making complex topics accessible in a finite time frame of 3 hours. It provides a solid foundation for learners starting their journey into ML with Python.

• Skill Enhancement Across Levels: Not limited to beginners, the course is also beneficial for intermediate and advanced programmers looking to add Python to their repertoire or enhance existing Python skills.

• Versatile Teaching Style: The course's approach to teaching Python and ML is commended for being clear, concise, and exact, suitable for learners with different backgrounds.

• Broad Topic Coverage: The syllabus covers a wide range of topics within the scope of 3 hours, including data analysis, visualization, and machine learning concepts.

• Real-World Application: The course is appreciated for its applicability to real-world scenarios and for helping learners to produce marketable skills.

• Cross-Programming Benefits: Learners with prior experience in languages like C++ and MATLAB find the course particularly useful, as it saves time by translating concepts between programming languages.

• Preparation for Further Learning: The course is seen as a valuable stepping stone for those looking to pursue more specialized courses, such as nVidia's Fundamentals of Deep Learning.

Cons:

• Complexity of Examples: Some learners find the examples provided in the course to be complex, which might hinder beginners who are still grasping the basics. A bit more introductory information on the packages and functions used, such as Pandas, Matlib, and Tensorflow, would be helpful.

• Technical Issues: There are occasional technical issues reported, with some learners experiencing blank screens during video playback.

• Minor Outdated Content: Some sections of the course may have outdated references to tabs and sections of Google Colab, which could lead to confusion if not updated.

• Python Syntax Recall: While the course helps recall what one has learned from Python, it might benefit from more explicit syntax explanations and examples to reinforce learning.

Additional Feedback:

• Some learners suggest that including examples for college statistics learning could be beneficial, as it would help bring back relevant concepts and make TensorFlow learning more accessible.

• Learners also express a desire for the course to run on different Python platforms, not just Google Colab.

Conclusion:

Overall, this course is highly recommended for its effectiveness in teaching Python with a focus on machine learning within a short duration. It is suitable for learners at various levels of programming proficiency, from beginners to advanced practitioners looking to expand their skill set. Despite some technical issues and the need for slight updates in content, the course remains a valuable asset for anyone interested in Python and ML, provided that learners are prepared to navigate through occasional challenges. The positive reviews from a wide range of learners speak volumes about the quality and impact of this course.